"""
Corresponding Chromaticities Prediction
=======================================

Define the objects to compute corresponding chromaticities prediction.

References
----------
-   :cite:`Breneman1987b` : Breneman, E. J. (1987). Corresponding
    chromaticities for different states of adaptation to complex visual fields.
    Journal of the Optical Society of America A, 4(6), 1115.
    doi:10.1364/JOSAA.4.001115
-   :cite:`CIETC1-321994b` : CIE TC 1-32. (1994). CIE 109-1994 A Method of
    Predicting Corresponding Colours under Different Chromatic and Illuminance
    Adaptations. Commission Internationale de l'Eclairage.
    ISBN:978-3-900734-51-0
-   :cite:`Fairchild1991a` : Fairchild, M. D. (1991). Formulation and testing
    of an incomplete-chromatic-adaptation model. Color Research & Application,
    16(4), 243-250. doi:10.1002/col.5080160406
-   :cite:`Fairchild2013s` : Fairchild, M. D. (2013). FAIRCHILD'S 1990 MODEL.
    In Color Appearance Models (3rd ed., pp. 4418-4495). Wiley. ISBN:B00DAYO8E2
-   :cite:`Fairchild2013t` : Fairchild, M. D. (2013). Chromatic Adaptation
    Models. In Color Appearance Models (3rd ed., pp. 4179-4252). Wiley.
    ISBN:B00DAYO8E2
-   :cite:`Li2002a` : Li, C., Luo, M. R., Rigg, B., & Hunt, R. W. G. (2002).
    CMC 2000 chromatic adaptation transform: CMCCAT2000. Color Research &
    Application, 27(1), 49-58. doi:10.1002/col.10005
-   :cite:`Luo1999` : Luo, M. Ronnier, & Rhodes, P. A. (1999).
    Corresponding-colour datasets. Color Research & Application, 24(4),
    295-296. doi:10.1002/(SICI)1520-6378(199908)24:4<295::AID-COL10>3.0.CO;2-K
-   :cite:`Westland2012k` : Westland, S., Ripamonti, C., & Cheung, V. (2012).
    CMCCAT2000. In Computational Colour Science Using MATLAB (2nd ed., pp.
    83-86). ISBN:978-0-470-66569-5
-   :cite:`Zhai2018` : Zhai, Q., & Luo, M. R. (2018). Study of chromatic
    adaptation via neutral white matches on different viewing media. Optics
    Express, 26(6), 7724. doi:10.1364/OE.26.007724
"""

from __future__ import annotations

from collections import namedtuple

import numpy as np

from colour.adaptation import (
    chromatic_adaptation_CIE1994,
    chromatic_adaptation_CMCCAT2000,
    chromatic_adaptation_Fairchild1990,
    chromatic_adaptation_VonKries,
    chromatic_adaptation_Zhai2018,
)
from colour.corresponding import (
    BRENEMAN_EXPERIMENT_PRIMARIES_CHROMATICITIES,
    BRENEMAN_EXPERIMENTS,
)
from colour.hints import (
    Any,
    ArrayLike,
    Literal,
    LiteralChromaticAdaptationTransform,
    Tuple,
)
from colour.models import (
    Luv_to_uv,
    Luv_uv_to_xy,
    XYZ_to_Luv,
    XYZ_to_xy,
    xy_to_XYZ,
    xyY_to_XYZ,
)
from colour.utilities import (
    CanonicalMapping,
    as_float_scalar,
    attest,
    domain_range_scale,
    filter_kwargs,
    full,
)

__author__ = "Colour Developers"
__copyright__ = "Copyright 2013 Colour Developers"
__license__ = "BSD-3-Clause - https://opensource.org/licenses/BSD-3-Clause"
__maintainer__ = "Colour Developers"
__email__ = "colour-developers@colour-science.org"
__status__ = "Production"

__all__ = [
    "CorrespondingColourDataset",
    "CorrespondingChromaticitiesPrediction",
    "convert_experiment_results_Breneman1987",
    "corresponding_chromaticities_prediction_Fairchild1990",
    "corresponding_chromaticities_prediction_CIE1994",
    "corresponding_chromaticities_prediction_CMCCAT2000",
    "corresponding_chromaticities_prediction_VonKries",
    "corresponding_chromaticities_prediction_Zhai2018",
    "CORRESPONDING_CHROMATICITIES_PREDICTION_MODELS",
    "corresponding_chromaticities_prediction",
]


class CorrespondingColourDataset(
    namedtuple(
        "CorrespondingColourDataset",
        (
            "name",
            "XYZ_r",
            "XYZ_t",
            "XYZ_cr",
            "XYZ_ct",
            "Y_r",
            "Y_t",
            "B_r",
            "B_t",
            "metadata",
        ),
    )
):
    """
    Define a corresponding colour dataset.

    Parameters
    ----------
    name
        Corresponding colour dataset name.
    XYZ_r
        *CIE XYZ* tristimulus values of the reference illuminant.
    XYZ_t
        *CIE XYZ* tristimulus values of the test illuminant.
    XYZ_cr
        Corresponding *CIE XYZ* tristimulus values under the reference
        illuminant.
    XYZ_ct
        Corresponding *CIE XYZ* tristimulus values under the test illuminant.
    Y_r
        Reference white luminance :math:`Y_r` in :math:`cd/m^2`.
    Y_t
        Test white luminance :math:`Y_t` in :math:`cd/m^2`.
    B_r
         Luminance factor :math:`B_r` of reference achromatic background as
         percentage.
    B_t
         Luminance factor :math:`B_t` of test achromatic background as
         percentage.
    metadata
        Dataset metadata.

    Notes
    -----
    -   This class is compatible with *Luo and Rhodes (1999)*
        *Corresponding-Colour Datasets* datasets.

    References
    ----------
    :cite:`Luo1999`
    """


class CorrespondingChromaticitiesPrediction(
    namedtuple(
        "CorrespondingChromaticitiesPrediction",
        ("name", "uv_t", "uv_m", "uv_p"),
    )
):
    """
    Define a chromatic adaptation model prediction.

    Parameters
    ----------
    name
        Test colour name.
    uv_t
        Chromaticity coordinates :math:`uv_t^p` of test colour.
    uv_m
        Chromaticity coordinates :math:`uv_m^p` of matching colour.
    uv_p
        Chromaticity coordinates :math:`uv_p^p` of predicted colour.
    """


def convert_experiment_results_Breneman1987(
    experiment: Literal[1, 2, 3, 4, 6, 8, 9, 11, 12],
) -> CorrespondingColourDataset:
    """
    Convert *Breneman (1987)* experiment results to a
    :class:`colour.CorrespondingColourDataset` class instance.

    Parameters
    ----------
    experiment
        *Breneman (1987)* experiment number.

    Returns
    -------
    :class:`colour.CorrespondingColourDataset`
        :class:`colour.CorrespondingColourDataset` class instance.

    Examples
    --------
    >>> from pprint import pprint
    >>> pprint(tuple(convert_experiment_results_Breneman1987(2)))
    ... # doctest: +ELLIPSIS
    (2,
     array([ 0.9582463...,  1.        ,  0.9436325...]),
     array([ 0.9587332...,  1.        ,  0.4385796...]),
     array([[ 388.125     ,  405.        ,  345.625     ],
           [ 266.8957925...,  135.        ,   28.5983365...],
           [ 474.5717821...,  405.        ,  222.75     ...],
           [ 538.3899082...,  405.        ,   24.8944954...],
           [ 178.7430167...,  135.        ,   19.6089385...],
           [ 436.6749547...,  405.        ,   26.5483725...],
           [ 124.7746282...,  135.        ,   36.1965613...],
           [  77.0794172...,  135.        ,   60.5850563...],
           [ 279.9390889...,  405.        ,  455.8395127...],
           [ 149.5808157...,  135.        ,  498.7046827...],
           [ 372.1113689...,  405.        ,  669.9883990...],
           [ 212.3638968...,  135.        ,  414.6704871...]]),
     array([[ 400.1039651...,  405.        ,  191.7287234...],
           [ 271.0384615...,  135.        ,   13.5      ...],
           [ 495.4705323...,  405.        ,  119.7290874...],
           [ 580.7967033...,  405.        ,    6.6758241...],
           [ 190.1933701...,  135.        ,    7.4585635...],
           [ 473.7184115...,  405.        ,   10.2346570...],
           [ 135.4936014...,  135.        ,   20.2376599...],
           [  86.4689781...,  135.        ,   35.2281021...],
           [ 283.5396281...,  405.        ,  258.1775929...],
           [ 119.7044335...,  135.        ,  282.6354679...],
           [ 359.9532224...,  405.        ,  381.0031185...],
           [ 181.8271461...,  135.        ,  204.0661252...]]),
     1500.0,
     1500.0,
     0.3,
     0.3,
     {})
    """

    valid_experiment_results = [1, 2, 3, 4, 6, 8, 9, 11, 12]
    attest(
        experiment in valid_experiment_results,
        f'"Breneman (1987)" experiment result is invalid, it must be one of '
        f'"{valid_experiment_results}"!',
    )

    samples_luminance = [
        0.270,
        0.090,
        0.270,
        0.270,
        0.090,
        0.270,
        0.090,
        0.090,
        0.270,
        0.090,
        0.270,
        0.090,
    ]

    experiment_results = list(BRENEMAN_EXPERIMENTS[experiment])
    illuminant_chromaticities = experiment_results.pop(0)
    Y_r = Y_t = as_float_scalar(
        BRENEMAN_EXPERIMENT_PRIMARIES_CHROMATICITIES[experiment].Y
    )
    B_r = B_t = 0.3

    XYZ_t, XYZ_r = (
        xy_to_XYZ(
            np.hstack(
                [
                    Luv_uv_to_xy(illuminant_chromaticities[1:3]),
                    full((2, 1), Y_r),
                ]
            )
        )
        / Y_r
    )

    xyY_cr, xyY_ct = [], []
    for i, experiment_result in enumerate(experiment_results):
        xyY_cr.append(
            np.hstack(
                [
                    Luv_uv_to_xy(experiment_result[2]),
                    samples_luminance[i] * Y_r,
                ]
            )
        )
        xyY_ct.append(
            np.hstack(
                [
                    Luv_uv_to_xy(experiment_result[1]),
                    samples_luminance[i] * Y_t,
                ]
            )
        )

    XYZ_cr = xyY_to_XYZ(xyY_cr)
    XYZ_ct = xyY_to_XYZ(xyY_ct)

    return CorrespondingColourDataset(
        experiment, XYZ_r, XYZ_t, XYZ_cr, XYZ_ct, Y_r, Y_t, B_r, B_t, {}
    )


def corresponding_chromaticities_prediction_Fairchild1990(
    experiment: (Literal[1, 2, 3, 4, 6, 8, 9, 11, 12] | CorrespondingColourDataset) = 1,
) -> Tuple[CorrespondingChromaticitiesPrediction, ...]:
    """
    Return the corresponding chromaticities prediction for *Fairchild (1990)*
    chromatic adaptation model.

    Parameters
    ----------
    experiment
        *Breneman (1987)* experiment number or
        :class:`colour.CorrespondingColourDataset` class instance.

    Returns
    -------
    :class:`tuple`
        Corresponding chromaticities prediction.

    References
    ----------
    :cite:`Breneman1987b`, :cite:`Fairchild1991a`, :cite:`Fairchild2013s`

    Examples
    --------
    >>> from pprint import pprint
    >>> pr = corresponding_chromaticities_prediction_Fairchild1990(2)
    >>> pr = [(p.uv_m, p.uv_p) for p in pr]
    >>> pprint(pr)  # doctest: +ELLIPSIS
    [(array([ 0.207,  0.486]), array([ 0.2089528...,  0.4724034...])),
     (array([ 0.449,  0.511]), array([ 0.4375652...,  0.5121030...])),
     (array([ 0.263,  0.505]), array([ 0.2621362...,  0.4972538...])),
     (array([ 0.322,  0.545]), array([ 0.3235312...,  0.5475665...])),
     (array([ 0.316,  0.537]), array([ 0.3151391...,  0.5398333...])),
     (array([ 0.265,  0.553]), array([ 0.2634745...,  0.5544335...])),
     (array([ 0.221,  0.538]), array([ 0.2211595...,  0.5324470...])),
     (array([ 0.135,  0.532]), array([ 0.1396949...,  0.5207234...])),
     (array([ 0.145,  0.472]), array([ 0.1512288...,  0.4533041...])),
     (array([ 0.163,  0.331]), array([ 0.1715691...,  0.3026264...])),
     (array([ 0.176,  0.431]), array([ 0.1825792...,  0.4077892...])),
     (array([ 0.244,  0.349]), array([ 0.2418905...,  0.3413401...]))]
    """

    experiment_results = (
        experiment
        if isinstance(experiment, CorrespondingColourDataset)
        else convert_experiment_results_Breneman1987(experiment)
    )

    with domain_range_scale("1"):
        XYZ_t, XYZ_r = experiment_results.XYZ_t, experiment_results.XYZ_r
        xy_t, xy_r = XYZ_to_xy([XYZ_t, XYZ_r])

        uv_t = Luv_to_uv(XYZ_to_Luv(experiment_results.XYZ_ct, xy_t), xy_t)
        uv_m = Luv_to_uv(XYZ_to_Luv(experiment_results.XYZ_cr, xy_r), xy_r)

        Y_n = experiment_results.Y_t

        XYZ_1 = experiment_results.XYZ_ct
        XYZ_2 = chromatic_adaptation_Fairchild1990(XYZ_1, XYZ_t, XYZ_r, Y_n)
        uv_p = Luv_to_uv(XYZ_to_Luv(XYZ_2, xy_r), xy_r)

        return tuple(
            CorrespondingChromaticitiesPrediction(
                experiment_results.name, uv_t[i], uv_m[i], uv_p[i]
            )
            for i in range(len(uv_t))
        )


def corresponding_chromaticities_prediction_CIE1994(
    experiment: (Literal[1, 2, 3, 4, 6, 8, 9, 11, 12] | CorrespondingColourDataset) = 1,
) -> Tuple[CorrespondingChromaticitiesPrediction, ...]:
    """
    Return the corresponding chromaticities prediction for *CIE 1994*
    chromatic adaptation model.

    Parameters
    ----------
    experiment
        *Breneman (1987)* experiment number or
        :class:`colour.CorrespondingColourDataset` class instance.

    Returns
    -------
    :class:`tuple`
        Corresponding chromaticities prediction.

    References
    ----------
    :cite:`Breneman1987b`, :cite:`CIETC1-321994b`

    Examples
    --------
    >>> from pprint import pprint
    >>> pr = corresponding_chromaticities_prediction_CIE1994(2)
    >>> pr = [(p.uv_m, p.uv_p) for p in pr]
    >>> pprint(pr)  # doctest: +ELLIPSIS
    [(array([ 0.207,  0.486]), array([ 0.2273130...,  0.5267609...])),
     (array([ 0.449,  0.511]), array([ 0.4612181...,  0.5191849...])),
     (array([ 0.263,  0.505]), array([ 0.2872404...,  0.5306938...])),
     (array([ 0.322,  0.545]), array([ 0.3489822...,  0.5454398...])),
     (array([ 0.316,  0.537]), array([ 0.3371612...,  0.5421567...])),
     (array([ 0.265,  0.553]), array([ 0.2889416...,  0.5534074...])),
     (array([ 0.221,  0.538]), array([ 0.2412195...,  0.5464301...])),
     (array([ 0.135,  0.532]), array([ 0.1530344...,  0.5488239...])),
     (array([ 0.145,  0.472]), array([ 0.1568709...,  0.5258835...])),
     (array([ 0.163,  0.331]), array([ 0.1499762...,  0.4401747...])),
     (array([ 0.176,  0.431]), array([ 0.1876711...,  0.5039627...])),
     (array([ 0.244,  0.349]), array([ 0.2560012...,  0.4546263...]))]
    """

    experiment_results = (
        experiment
        if isinstance(experiment, CorrespondingColourDataset)
        else convert_experiment_results_Breneman1987(experiment)
    )

    with domain_range_scale("1"):
        XYZ_t, XYZ_r = experiment_results.XYZ_t, experiment_results.XYZ_r
        xy_o1, xy_o2 = XYZ_to_xy([XYZ_t, XYZ_r])

        uv_t = Luv_to_uv(XYZ_to_Luv(experiment_results.XYZ_ct, xy_o1), xy_o1)
        uv_m = Luv_to_uv(XYZ_to_Luv(experiment_results.XYZ_cr, xy_o2), xy_o2)

        Y_r = experiment_results.B_r
        E_o1, E_o2 = experiment_results.Y_t, experiment_results.Y_r

        XYZ_1 = experiment_results.XYZ_ct
        XYZ_2 = chromatic_adaptation_CIE1994(XYZ_1, xy_o1, xy_o2, Y_r, E_o1, E_o2)
        uv_p = Luv_to_uv(XYZ_to_Luv(XYZ_2, xy_o2), xy_o2)

        return tuple(
            CorrespondingChromaticitiesPrediction(
                experiment_results.name, uv_t[i], uv_m[i], uv_p[i]
            )
            for i in range(len(uv_t))
        )


def corresponding_chromaticities_prediction_CMCCAT2000(
    experiment: (Literal[1, 2, 3, 4, 6, 8, 9, 11, 12] | CorrespondingColourDataset) = 1,
) -> Tuple[CorrespondingChromaticitiesPrediction, ...]:
    """
    Return the corresponding chromaticities prediction for *CMCCAT2000*
    chromatic adaptation model.

    Parameters
    ----------
    experiment
        *Breneman (1987)* experiment number or
        :class:`colour.CorrespondingColourDataset` class instance.

    Returns
    -------
    :class:`tuple`
        Corresponding chromaticities prediction.

    References
    ----------
    :cite:`Breneman1987b`, :cite:`Li2002a`, :cite:`Westland2012k`

    Examples
    --------
    >>> from pprint import pprint
    >>> pr = corresponding_chromaticities_prediction_CMCCAT2000(2)
    >>> pr = [(p.uv_m, p.uv_p) for p in pr]
    >>> pprint(pr)  # doctest: +ELLIPSIS
    [(array([ 0.207,  0.486]), array([ 0.2083210...,  0.4727168...])),
     (array([ 0.449,  0.511]), array([ 0.4459270...,  0.5077735...])),
     (array([ 0.263,  0.505]), array([ 0.2640262...,  0.4955361...])),
     (array([ 0.322,  0.545]), array([ 0.3316884...,  0.5431580...])),
     (array([ 0.316,  0.537]), array([ 0.3222624...,  0.5357624...])),
     (array([ 0.265,  0.553]), array([ 0.2710705...,  0.5501997...])),
     (array([ 0.221,  0.538]), array([ 0.2261826...,  0.5294740...])),
     (array([ 0.135,  0.532]), array([ 0.1439693...,  0.5190984...])),
     (array([ 0.145,  0.472]), array([ 0.1494835...,  0.4556760...])),
     (array([ 0.163,  0.331]), array([ 0.1563172...,  0.3164151...])),
     (array([ 0.176,  0.431]), array([ 0.1763199...,  0.4127589...])),
     (array([ 0.244,  0.349]), array([ 0.2287638...,  0.3499324...]))]
    """

    experiment_results = (
        experiment
        if isinstance(experiment, CorrespondingColourDataset)
        else convert_experiment_results_Breneman1987(experiment)
    )

    with domain_range_scale("1"):
        XYZ_w, XYZ_wr = experiment_results.XYZ_t, experiment_results.XYZ_r
        xy_w, xy_wr = XYZ_to_xy([XYZ_w, XYZ_wr])

        uv_t = Luv_to_uv(XYZ_to_Luv(experiment_results.XYZ_ct, xy_w), xy_w)
        uv_m = Luv_to_uv(XYZ_to_Luv(experiment_results.XYZ_cr, xy_wr), xy_wr)

        L_A1 = experiment_results.Y_t
        L_A2 = experiment_results.Y_r

        XYZ_1 = experiment_results.XYZ_ct
        XYZ_2 = chromatic_adaptation_CMCCAT2000(XYZ_1, XYZ_w, XYZ_wr, L_A1, L_A2)
        uv_p = Luv_to_uv(XYZ_to_Luv(XYZ_2, xy_wr), xy_wr)

        return tuple(
            CorrespondingChromaticitiesPrediction(
                experiment_results.name, uv_t[i], uv_m[i], uv_p[i]
            )
            for i in range(len(uv_t))
        )


def corresponding_chromaticities_prediction_VonKries(
    experiment: (Literal[1, 2, 3, 4, 6, 8, 9, 11, 12] | CorrespondingColourDataset) = 1,
    transform: LiteralChromaticAdaptationTransform | str = "CAT02",
) -> Tuple[CorrespondingChromaticitiesPrediction, ...]:
    """
    Return the corresponding chromaticities prediction for *Von Kries*
    chromatic adaptation model using given transform.

    Parameters
    ----------
    experiment
        *Breneman (1987)* experiment number or
        :class:`colour.CorrespondingColourDataset` class instance.
    transform
        Chromatic adaptation transform.

    Returns
    -------
    :class:`tuple`
        Corresponding chromaticities prediction.

    References
    ----------
    :cite:`Breneman1987b`, :cite:`Fairchild2013t`

    Examples
    --------
    >>> from pprint import pprint
    >>> pr = corresponding_chromaticities_prediction_VonKries(2, "Bradford")
    >>> pr = [(p.uv_m, p.uv_p) for p in pr]
    >>> pprint(pr)  # doctest: +ELLIPSIS
    [(array([ 0.207,  0.486]), array([ 0.2082014...,  0.4722922...])),
     (array([ 0.449,  0.511]), array([ 0.4489102...,  0.5071602...])),
     (array([ 0.263,  0.505]), array([ 0.2643545...,  0.4959631...])),
     (array([ 0.322,  0.545]), array([ 0.3348730...,  0.5471220...])),
     (array([ 0.316,  0.537]), array([ 0.3248758...,  0.5390589...])),
     (array([ 0.265,  0.553]), array([ 0.2733105...,  0.5555028...])),
     (array([ 0.221,  0.538]), array([ 0.227148 ...,  0.5331318...)),
     (array([ 0.135,  0.532]), array([ 0.1442730...,  0.5226804...])),
     (array([ 0.145,  0.472]), array([ 0.1498745...,  0.4550785...])),
     (array([ 0.163,  0.331]), array([ 0.1564975...,  0.3148796...])),
     (array([ 0.176,  0.431]), array([ 0.1760593...,  0.4103772...])),
     (array([ 0.244,  0.349]), array([ 0.2259805...,  0.3465291...]))]
    """

    experiment_results = (
        experiment
        if isinstance(experiment, CorrespondingColourDataset)
        else convert_experiment_results_Breneman1987(experiment)
    )

    with domain_range_scale("1"):
        XYZ_w, XYZ_wr = experiment_results.XYZ_t, experiment_results.XYZ_r
        xy_w, xy_wr = XYZ_to_xy([XYZ_w, XYZ_wr])

        uv_t = Luv_to_uv(XYZ_to_Luv(experiment_results.XYZ_ct, xy_w), xy_w)
        uv_m = Luv_to_uv(XYZ_to_Luv(experiment_results.XYZ_cr, xy_wr), xy_wr)

        XYZ_1 = experiment_results.XYZ_ct
        XYZ_2 = chromatic_adaptation_VonKries(XYZ_1, XYZ_w, XYZ_wr, transform)
        uv_p = Luv_to_uv(XYZ_to_Luv(XYZ_2, xy_wr), xy_wr)

        return tuple(
            CorrespondingChromaticitiesPrediction(
                experiment_results.name, uv_t[i], uv_m[i], uv_p[i]
            )
            for i in range(len(uv_t))
        )


def corresponding_chromaticities_prediction_Zhai2018(
    experiment: (Literal[1, 2, 3, 4, 6, 8, 9, 11, 12] | CorrespondingColourDataset) = 1,
    D_b: ArrayLike = 1,
    D_d: ArrayLike = 1,
    XYZ_wo: ArrayLike = np.array([1, 1, 1]),
    transform: Literal["CAT02", "CAT16"] | str = "CAT02",
) -> Tuple[CorrespondingChromaticitiesPrediction, ...]:
    """
    Return the corresponding chromaticities prediction for
    *Zhai and Luo (2018)* chromatic adaptation model using given transform.

    Parameters
    ----------
    experiment
        *Breneman (1987)* experiment number or
        :class:`colour.CorrespondingColourDataset` class instance.
    D_b
        Degree of adaptation :math:`D_{\\beta}` of input illuminant
        :math:`\\beta`.
    D_d
        Degree of adaptation :math:`D_{\\delta}` of output illuminant
        :math:`\\delta`.
    XYZ_wo
        Baseline illuminant (:math:`BI`) :math:`o`.
    transform
        Chromatic adaptation transform.

    Returns
    -------
    :class:`tuple`
        Corresponding chromaticities prediction.

    References
    ----------
    :cite:`Breneman1987b`, :cite:`Zhai2018`

    Examples
    --------
    >>> from pprint import pprint
    >>> pr = corresponding_chromaticities_prediction_Zhai2018(2)
    >>> pr = [(p.uv_m, p.uv_p) for p in pr]
    >>> pprint(pr)  # doctest: +ELLIPSIS
    [(array([ 0.207,  0.486]), array([ 0.2082238...,  0.4727943...])),
     (array([ 0.449,  0.511]), array([ 0.4474691...,  0.5076681...])),
     (array([ 0.263,  0.505]), array([ 0.2640379...,  0.4954003...])),
     (array([ 0.322,  0.545]), array([ 0.3336937...,  0.5435500...])),
     (array([ 0.316,  0.537]), array([ 0.3238490...,  0.5359889...])),
     (array([ 0.265,  0.553]), array([ 0.2724846...,  0.5506939...])),
     (array([ 0.221,  0.538]), array([ 0.2267596...,  0.5295259...])),
     (array([ 0.135,  0.532]), array([ 0.1443208...,  0.5190035...])),
     (array([ 0.145,  0.472]), array([ 0.1500723...,  0.4561352...])),
     (array([ 0.163,  0.331]), array([ 0.1570902...,  0.3245137...])),
     (array([ 0.176,  0.431]), array([ 0.1763887...,  0.4146000...])),
     (array([ 0.244,  0.349]), array([ 0.2267005...,  0.3551480...]))]
    """

    experiment_results = (
        experiment
        if isinstance(experiment, CorrespondingColourDataset)
        else convert_experiment_results_Breneman1987(experiment)
    )

    with domain_range_scale("1"):
        XYZ_w, XYZ_wr = experiment_results.XYZ_t, experiment_results.XYZ_r
        xy_w, xy_wr = XYZ_to_xy([XYZ_w, XYZ_wr])

        uv_t = Luv_to_uv(XYZ_to_Luv(experiment_results.XYZ_ct, xy_w), xy_w)
        uv_m = Luv_to_uv(XYZ_to_Luv(experiment_results.XYZ_cr, xy_wr), xy_wr)

        XYZ_1 = experiment_results.XYZ_ct
        XYZ_2 = chromatic_adaptation_Zhai2018(
            XYZ_1, XYZ_w, XYZ_wr, D_b, D_d, XYZ_wo, transform
        )
        uv_p = Luv_to_uv(XYZ_to_Luv(XYZ_2, xy_wr), xy_wr)

        return tuple(
            CorrespondingChromaticitiesPrediction(
                experiment_results.name, uv_t[i], uv_m[i], uv_p[i]
            )
            for i in range(len(uv_t))
        )


CORRESPONDING_CHROMATICITIES_PREDICTION_MODELS = CanonicalMapping(
    {
        "CIE 1994": corresponding_chromaticities_prediction_CIE1994,
        "CMCCAT2000": corresponding_chromaticities_prediction_CMCCAT2000,
        "Fairchild 1990": corresponding_chromaticities_prediction_Fairchild1990,
        "Von Kries": corresponding_chromaticities_prediction_VonKries,
        "Zhai 2018": corresponding_chromaticities_prediction_Zhai2018,
    }
)
CORRESPONDING_CHROMATICITIES_PREDICTION_MODELS.__doc__ = """
Aggregated corresponding chromaticities prediction models.

References
----------
:cite:`Breneman1987b`, :cite:`CIETC1-321994b`, :cite:`Fairchild1991a`,
:cite:`Fairchild2013s`, :cite:`Fairchild2013t`, :cite:`Li2002a`,
:cite:`Westland2012k`, :cite:`Zhai2018`

Aliases:

-   'vonkries': 'Von Kries'
"""
CORRESPONDING_CHROMATICITIES_PREDICTION_MODELS[
    "vonkries"
] = CORRESPONDING_CHROMATICITIES_PREDICTION_MODELS["Von Kries"]


def corresponding_chromaticities_prediction(
    experiment: (Literal[1, 2, 3, 4, 6, 8, 9, 11, 12] | CorrespondingColourDataset) = 1,
    model: (
        Literal[
            "CIE 1994",
            "CMCCAT2000",
            "Fairchild 1990",
            "Von Kries",
            "Zhai 2018",
        ]
        | str
    ) = "Von Kries",
    **kwargs: Any,
) -> Tuple[CorrespondingChromaticitiesPrediction, ...]:
    """
    Return the corresponding chromaticities prediction for given chromatic
    adaptation model.

    Parameters
    ----------
    experiment
        *Breneman (1987)* experiment number or
        :class:`colour.CorrespondingColourDataset` class instance.
    model
        Chromatic adaptation model.

    Other Parameters
    ----------------
    D_b
        {:func:`colour.corresponding.\
corresponding_chromaticities_prediction_Zhai2018`},
        Degree of adaptation :math:`D_{\\beta}` of input illuminant
        :math:`\\beta`.
    D_d
        {:func:`colour.corresponding.\
corresponding_chromaticities_prediction_Zhai2018`},
        Degree of adaptation :math:`D_{\\delta}` of output illuminant
        :math:`\\delta`.
    transform
        {:func:`colour.corresponding.\
corresponding_chromaticities_prediction_VonKries`,
        :func:`colour.corresponding.\
corresponding_chromaticities_prediction_Zhai2018`},
        Chromatic adaptation transform.
    XYZ_wo
        {:func:`colour.corresponding.\
corresponding_chromaticities_prediction_Zhai2018`},
        Baseline illuminant (:math:`BI`) :math:`o`.

    Returns
    -------
    :class:`tuple`
        Corresponding chromaticities prediction.

    References
    ----------
    :cite:`Breneman1987b`, :cite:`CIETC1-321994b`, :cite:`Fairchild1991a`,
    :cite:`Fairchild2013s`, :cite:`Fairchild2013t`, :cite:`Li2002a`,
    :cite:`Westland2012k`, :cite:`Zhai2018`

    Examples
    --------
    >>> from pprint import pprint
    >>> pr = corresponding_chromaticities_prediction(2, "CMCCAT2000")
    >>> pr = [(p.uv_m, p.uv_p) for p in pr]
    >>> pprint(pr)  # doctest: +SKIP
    [((0.207, 0.486), (0.2083210..., 0.4727168...)),
     ((0.449, 0.511), (0.4459270..., 0.5077735...)),
     ((0.263, 0.505), (0.2640262..., 0.4955361...)),
     ((0.322, 0.545), (0.3316884..., 0.5431580...)),
     ((0.316, 0.537), (0.3222624..., 0.5357624...)),
     ((0.265, 0.553), (0.2710705..., 0.5501997...)),
     ((0.221, 0.538), (0.2261826..., 0.5294740...)),
     ((0.135, 0.532), (0.1439693..., 0.5190984...)),
     ((0.145, 0.472), (0.1494835..., 0.4556760...)),
     ((0.163, 0.331), (0.1563172..., 0.3164151...)),
     ((0.176, 0.431), (0.1763199..., 0.4127589...)),
     ((0.244, 0.349), (0.2287638..., 0.3499324...))]
    """

    function = CORRESPONDING_CHROMATICITIES_PREDICTION_MODELS[model]

    return function(experiment, **filter_kwargs(function, **kwargs))
